Personalized Travel Recommendation System on Android Platform

Millions of travellers use Skyscanner every day. Skyscanner Flights helps to find the optimal flight on Android, iOS and web by scanning through thousands of providers and carriers in real time.

Nowadays, more and more companies improve their systems further by utilizing Big Data and Artificial Intelligence technologies. Users expect the systems to help reaching their goals by understanding their habits and goals.

In my thesis, I planned and implemented a complex personalized travel recommendation system that understands the user and provides useful travel insights. First, I modelled the users and attempted both client and server side modelling. I designed and developed a complex system that is able to consume user generated data from Skyscanner's already running backend services and recommend travel options based on it. This system helps the users to find the best travel options by providing personalized recommendations. I deployed the system to Amazon Web Services. I made the proposals available in the Skyscanner Android application.